Peer-reviewedCC BY-NC-NDGenome medicine
Spatial mixed models for assessing environmental exposure effects on the microbiome.
7 affiliations
- 1Division of Biostatistics, Department of Population Health, Grossman School of Medicine, New York University, 180 Madison Avenue, 4th Floor, New York, NY, 10016, USA.
- 2School of Dentistry, Department of Microbiology and Immunology, Kyungpook National University, Daegu, Korea.
- 3Division of Pulmonary, Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA.
- 4Hebrew University of Jerusalem, Jerusalem, Israel.
- 5Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA.
- 6NYU Laura and Isaac Perlmutter Cancer Center, New York, NY, USA.
- 7Division of Biostatistics, Department of Population Health, Grossman School of Medicine, New York University, 180 Madison Avenue, 4th Floor, New York, NY, 10016, USA. [email protected].
Abstract
Environmental exposures such as air pollution may influence human health partly through microbiome, but statistical modeling is challenged by spatial dependence across regions and ecological correlations among taxa. We propose SpaMixed, a novel spatial mixed modeling framework for microbiome count data that incorporates both sources of dependence using conditional autoregressive priors. In simulations, SpaMixed achieved high feature selection power while maintaining low false positive rates and reduced estimation error relative to existing methods. Applied to two real studies with fine particulate matter (PM2.5) exposures, SpaMixed identified biologically plausible PM2.5-associated taxa, supporting its utility for environmental microbiome research.
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BibTeX
@article{kim2026spatial,
title = {{Spatial mixed models for assessing environmental exposure effects on the microbiome.}},
author = {Sooran Kim and Chan Wang and Soyoung Kwak and Fares Darawshy and Alexander Bain and Leopoldo N Segal and Jiyoung Ahn and Huilin Li},
journal = {Genome medicine},
year = {2026},
month = sep,
doi = {10.1186/s13073-026-01770-3},
url = {https://europepmc.org/article/MED/42907900},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Spatial mixed models for assessing environmental exposure effects on the microbiome.
AU - Sooran Kim
AU - Chan Wang
AU - Soyoung Kwak
AU - Fares Darawshy
AU - Alexander Bain
AU - Leopoldo N Segal
AU - Jiyoung Ahn
AU - Huilin Li
PY - 2026
DA - 2026/09/01
JO - Genome medicine
DO - 10.1186/s13073-026-01770-3
UR - https://europepmc.org/article/MED/42907900
AB - Environmental exposures such as air pollution may influence human health partly through microbiome, but statistical modeling is challenged by spatial dependence across regions and ecological correlations among taxa. We propose SpaMixed, a novel spatial mixed modeling framework for microbiome count data that incorporates both sources of dependence using conditional autoregressive priors. In simulations, SpaMixed achieved high feature selection power while maintaining low false positive rates and reduced estimation error relative to existing methods. Applied to two real studies with fine particulate matter (PM2.5) exposures, SpaMixed identified biologically plausible PM2.5-associated taxa, supporting its utility for environmental microbiome research.
SN - 1756-994X
ER -